John S. Hollywood

Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations

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Using causal models in heterogeneous information fusion to detect terrorists

We describe basic research that uses a causal, uncertainty-sensitive computational model rooted in qualitative social science to fuse disparate pieces of threat information. It is a cognitive model going beyond rational-actor methods. Having such a model has proven useful when information is uncertain, fragmentary, indirect, soft, conflicting, and even deceptive. Inferences from fusion must then account for uncertainties about the model, the credibility of information, and the fusion methods - i.e. we must consider both structural and parametric uncertainties, including uncertainties about the uncertainties. We use a novel combination of (1) probabilistic and parametric methods, (2) alternative models and model structures, and (3) alternative fusion methods that include nonlinear algebraic combination, variants of Bayesian inference, and a new entropy-maximizing approach. Initial results are encouraging and suggest that such an analytically flexible and model-based approach to fusion can simultaneously enrich thinking, enhance threat detection, and reduce harmful false alarms.

Causal Models and Exploratory Analysis in Heterogeneous Information Fusion for Detecting Potential Terrorists

Abstract : We describe research fusing heterogeneous information in an effort eventually to detect terrorists, reduce false alarms, and exonerate those falsely identified. The specific research is more humble, using synthetic data and first versions of fusion methods. Both the information and the fusion methods are subject to deep uncertainty. The information may also be fragmentary, indirect, soft, conflicting, and even deceptive. We developed a research prototype of an analyst centric fusion platform. This uses (1) causal computational models rooted in social science to relate observable information about individuals to an estimate of the threat that the individual poses and (2) a battery of different methods to fuse across information reports. We account for uncertainties about the causal model, the information, and the fusion methods. We address structural and parametric uncertainties, including uncertainties about the uncertainties, at different levels of detail. We use a combination of (1) probabilistic and parametric methods, (2)alternative models, and (3) alternative fusion methods that include nonlinear algebraic combination, Bayesian inference, and an entropy-maximizing approach. This paper focuses primarily on dealing with deep uncertainty in multiple dimensions.

An adaptive scheduling framework for heterogeneous computer networks

Abstract This paper presents an adaptive scheduling framework for large-scale computer networks facing significant environmental dynamics such as shifts in job mix and available resources. The presented framework detects changes in the job stream, and other events, and evolves scheduling policies at two distinct levels—operational and tactical—in response to the changes, making adaptations such as reassigning resources and adjusting sequencing heuristics. The paper also presents a simulation of a computer network facing dynamic job arrival rates, which demonstrates that even a simple implementation of the framework can lead to major gains in performance.

Using Social Media and Social Network Analysis in Law Enforcement: Creating a Research Agenda, Including Business Cases, Protections, and Technology Needs

I n April 2017, the National Institute of Justice convened an expert panel to assess, and identify high-priority needs for, law enforcement’s use of two closely linked technologies that have potential to provide key information needed to address crime risks, hold offenders accountable, and ensure physical safety: social media analysis and social network analysis. Social media analysis consists of methods and tools to collect and analyze text, photos, video, and other material shared via social media systems, such as Facebook and Twitter. Social network analysis is a type of data analysis that investigates social relationships and structures as represented by networks (which can also be called graphs). Social media, given that it reflects relationships inherently, is a key source of data for social network analysis; conversely, social network analysis is one key type of social media analysis. In all, the panel discussed five core business cases for employing social media analysis and social network analysis in law enforcement:

Using Future Broadband Communications Technologies to Strengthen Law Enforcement

Forthcoming broadband communications technologies could provide dramatically increased capabilities for law enforcement. In September 2015, the RAND Corporation convened an expert panel for the National Institute of Justice (NIJ) to discuss how law enforcement can best leverage future communications capabilities anticipated to be fielded over the next 10 to 15 years while mitigating potential risks. The Broadband Communications Workshop assembled 41 experts on both law enforcement operations and broadband technologies, and collectively identified 68 needs for technology initiatives, including both technical and nonmateriel requirements. The top ten needs identified at the workshop are listed below. The most prominent theme of the workshop was supporting the emergence of a future broadband network in which